mlsanityData to 14 Oct 2026

XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification

arXiv 2208.00629 ↗First posted 1 Aug 2022cs.LG cs.AI cs.CVcited by 1

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Abstract (arXiv)

Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framework for image classification that consists of two algorithms. The first, XOOD-M, is completely unsupervised, while the second XOOD-L is self-supervised. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network in order to distinguish between in-distribution and OOD instances. We show experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD detection methods on many benchmark data sets in both efficiency and accuracy, reducing false-positive rate (FPR95) by 50%, while improving the inferencing time by an order of magnitude.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 1 Aug 2022.

Builds on

21 of 29 references.

References · 21

  1. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  2. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  3. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  4. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  5. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  6. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  7. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  8. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  9. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  10. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  11. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
  12. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
9 more
  1. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  2. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  3. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  4. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  5. 2018Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifierscited by 126
  6. 2019Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancycited by 112
  7. 2021MOOD: Multi-level Out-of-distribution Detectioncited by 94
  8. 2021No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasetscited by 31
  9. 2021Shifting Transformation Learning for Out-of-Distribution Detectioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The Invisible Gorilla Effect in Out-of-distribution Detectioncited by 0